Bioinspired underwater locomotion of light-driven liquid crystal gels
Bibliographic record
Abstract
Soft-bodied aquatic invertebrates, such as sea slugs and snails, are capable of diverse locomotion modes under water. Recapitulation of such multimodal aquatic locomotion in small-scale soft robots is challenging, due to difficulties in precise spatiotemporal control of deformations and inefficient underwater actuation of existing stimuli-responsive materials. Solving this challenge and devising efficient untethered manipulation of soft stimuli-responsive materials in the aquatic environment would significantly broaden their application potential in biomedical devices. We mimic locomotion modes common to sea invertebrates using monolithic liquid crystal gels (LCGs) with inherent light responsiveness and molecular anisotropy. We elicit diverse underwater locomotion modes, such as crawling, walking, jumping, and swimming, by local deformations induced by selective spatiotemporal light illumination. Our results underpin the pivotal role of the physicomechanical properties of LCGs in the realization of diverse modes of light-driven robotic underwater locomotion. We envisage that our results will introduce a toolbox for designing efficient untethered soft robots for fluidic environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".